Over the past decade, many researchers have come up with different implementations of
systems for decoding covert or imagined speech from EEG (electroencephalogram). They …

Filters are commonly used to reduce noise and improve data quality. Filter theory is part of a
scientist's training, yet the impact of filters on interpreting data is not always fully appreciated …

Neural oscillations are ubiquitous across recording methodologies and species, broadly
associated with cognitive tasks, and amenable to computational modelling that investigates …

Objective. Supervised learning paradigms are often limited by the amount of labeled data
that is available. This phenomenon is particularly problematic in clinically-relevant data …

Sleep stage classification constitutes an important preliminary exam in the diagnosis of
sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of …

Understanding how brains process sensory signals in natural environments is one of the key
goals of twenty-first century neuroscience. While brain imaging and invasive …

The brain contains both generalized and stimulus-type-specific representations of aversive
events, but models of how these are integrated and related to subjective experience are …

Background Electroencephalographic data are easily contaminated by signals of non-neural
origin. Independent component analysis (ICA) can help correct EEG data for such artifacts …

The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a
trend towards more powerful multivariate analysis methods. Often it is desired to interpret the …

Background In cognitive neuroscience the potential of deep neural networks (DNNs) for
solving complex classification tasks is yet to be fully exploited. The most limiting factor is that …